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Improved individual and population-level HbA1c estimation using CGM data and patient characteristics
Machine learning and linear regression models using CGM and participant data reduced HbA1c estimation error by up to 26% compared to the GMI formula, and exhibit superior performance in estimating the median of HbA1c at the cohort level, potentially of value for remote clinical trials interrupted by...
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| Publié dans: | J Diabetes Complications |
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| Auteurs principaux: | , , , , , |
| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
Elsevier Inc.
2021
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| Sujets: | |
| Accès en ligne: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8316291/ https://ncbi.nlm.nih.gov/pubmed/34127370 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.jdiacomp.2021.107950 |
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